scholarly journals Full Tensor Eigenvector Analysis on Air-Borne Magnetic Gradiometer Data for the Detection of Dipole-Like Magnetic Sources

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 1976 ◽  
Author(s):  
◽  
◽  
Keyword(s):  
Eigenvector Analysis

Parallel-Process (Simultaneous) Machining and its Stability

1999 ◽  
Author(s):  
O. Burak Ozdoganlar ◽  
William J. Endres
Keyword(s):  
Initial Conditions ◽  
General System ◽  
Parallel Process ◽  
Analytical Stability ◽  
Multiple Processes ◽  
Or Practice ◽  
Machining System ◽  
Eigenvector Analysis ◽  
The Stability ◽  
Machining Operations

Abstract This paper presents a mathematical perspective, to complement the intuitive or practice-oriented perspective, to classifying machining operations as parallel-process (simultaneous) or single-process in nature. Illustrative scenarios are provided to demonstrate how these two perspectives may lead in different situations to the same or different conclusions regarding process parallelism. A model representation of a general parallel-process machining system is presented, based on which the general parallel-process stability eigenvalue problem is formulated. For a special simplified case of the general system, analytical methods are employed to derive a fully analytical stability solution. Thorough study of this solution through eigenvector analysis sheds light on some fundamental phenomena of parallel-process machining stability, such as dependence of the stability solution on phasing of the initial conditions (disturbances). This establishes the importance, when employing numerical time-domain simulation for such analyses, of specifying initial conditions for the multiple processes to be arbitrarily phased so that correct results are achieved across all spindle speeds.

scholarly journals Spectrum: Fast density-aware spectral clustering for single and multi-omic data

2019 ◽  
Author(s):  
Christopher R. John ◽  
David Watson ◽  
Michael Barnes ◽  
Costantino Pitzalis ◽  
Myles J. Lewis
Keyword(s):  
Spectral Clustering ◽  
Personalised Medicine ◽  
Cell Types ◽  
Expression Data ◽  
Rna Seq ◽  
Diffusion Technique ◽  
Eigenvector Analysis ◽  
And Performance ◽  
And Diffusion ◽  
Omic Data

AbstractClustering of single or multi-omic data is key to developing personalised medicine and identifying new cell types. We present Spectrum, a fast spectral clustering method for single and multi-omic expression data. Spectrum is flexible and performs well on single-cell RNA-seq data. The method uses a new density-aware kernel that adapts to data scale and density. It uses a tensor product graph data integration and diffusion technique to reveal underlying structures and reduce noise. We developed a powerful method of eigenvector analysis to determine the number of clusters. Benchmarking Spectrum on 21 datasets demonstrated improvements in runtime and performance relative to other state-of-the-art methods.Contact:[email protected]

Sign in / Sign up

Export Citation Format

Share Document